Ultrasonographic device

ABSTRACT

It possible to provide an ultrasonogram having a preferable spatial resolution and signal/noise ratio. A signal dynamic range is measured so that selection between a linear interpolation or sinc function interpolation is adaptively performed for the signal or weighted averaging of the both is performed. It is possible to set in advance a depth position for switching between the linear interpolation and the sinc function interpolation in a storage unit ( 21 ) so that the two interpolation methods are switched from one to the other at the boundary of the depth position.

TECHNICAL FIELD

The present invention relates to a device that displays anultrasonogram.

BACKGROUND ART

Heretofore, a beam scanning method of emitting a beam from asmall-diameter aperture to scan a sectoral region for acquiring a broadfield of view has been widely employed in ultrasonography. Examples ofthe method include so-called sector scan and so-called convex scan. Abroader field of view is more advantageous to find a lesion. However, itis also true that a smaller aperture diameter may be more advantageousin some cases as follows. Specifically, the smaller surface area a probehas, the more advantageous it is in such cases as where an areaavailable for the probe contact is limited in a subject to be examined.Examples of such cases include where the probe needs to be pressedagainst a narrow area between ribs such as in imaging a heart. However,if a surface area of a probe is reduced, the number of rasters obtainedtherewith is also reduced. If scan conversion is performed on data witha reduced number of rasters so as to display the data on a videodisplay, the resultant image will be degraded. Patent Document 1discloses a method for preventing such image degradation. In thismethod, a received signal is converted into a complex signal, and a realpart and an imaginary part of the complex signal are individuallyinterpolated by using sinc functions.

[Patent Document 1] JP-A 11-9603

DISCLOSURE OF THE INVENTION Problem to be Solved by the Invention

However, a larger number of rasters are used to compute a singleinterpolated data point in interpolation using sinc functions than insimple linear interpolation. Accordingly, if data on a certain rasterincludes noise, sinc function interpolation expands an effect of thenoise to distant points. Thus, sinc function interpolation is notnecessarily optimal for data containing noise, so that a challengingissue of deciding whether to apply sinc function interpolation to acertain processing-target data remains unsolved.

An object of the present invention is to provide an ultrasonographicdevice capable of applying optimal interpolation to processing-targetdata to contribute to reduction of noise contained in rasters.

Means for Solving the Problem

According to the present invention, a signal dynamic range for eachacquired signal is measured to determine a point either for adaptivelyswitching between linear interpolation and sinc function interpolationor for employing a weighted average of interpolated values respectivelyobtained by these interpolation methods. The depth point for switchingbetween linear interpolation and sinc function interpolation may bepreviously stored in a certain unit, and an interpolation method for usemay be switched between these two methods at the interpolation methodtransition depth. Instead of completely switching between these twomethods at the interpolation method transition depth, a weighted sum ofinterpolated values respectively obtained by the two interpolationmethods may be used as an interpolated value.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional block diagram illustrating an example of anultrasonographic device according to the present invention.

FIG. 2 illustrates a mechanism of scan conversion.

FIG. 3 is a graph for illustrating linear interpolation.

FIG. 4 is a graph for illustrating sinc function interpolation.

FIG. 5 illustrates a beam width.

FIG. 6 shows examples of experimental data indicating interpolationresults.

FIG. 7 is a graph for illustrating a relation between a signal dynamicrange and a beam width.

FIG. 8 is a graph for illustrating a relation between a signal dynamicrange and a beam width.

FIG. 9 illustrates that an effect of noise varies depending on whatinterpolation method is employed.

FIG. 10 illustrates a signal dynamic range and how to switch aninterpolation method.

FIG. 11 illustrates that an attenuation rate varies depending on modesand an interpolation method transition point corresponds to each mode.

FIG. 12 illustrates changes in weighting functions for interpolationmethod transition.

FIG. 13 is a schematic diagram of an interface of transition pointcontrol.

FIG. 14 is a schematic diagram of another interface of transition pointcontrol.

FIG. 15 is a functional block diagram illustrating another example ofthe ultrasonographic device according to the present invention.

FIG. 16 is a functional block diagram illustrating still another exampleof the ultrasonographic device according to the present invention.

FIG. 17 illustrates a noise reduction filter.

FIG. 18 is a flowchart for controlling the noise reduction filter.

FIG. 19 illustrates a weighting function after application of the noisereduction filter.

DESCRIPTION OF SYMBOLS

1 ultrasonic probe 2 transmit/receive switch 3 transmission beam former4 controller 20 receiving beam former 21 depth storage unit 22interpolation method setting unit 23 scan converter 24 display 29 memory30 interpolation processor 31 noise reduction filter 32 diagnosticsystem main unit 33 interface of transition point control 101 parametersset process 102 weighting value computing area setting process 103weighting value computing process 104 completion decision process 105intensity adjustment process 106 decision process

BEST MODES FOR CARRYING OUT THE INVENTION

Hereinafter, embodiments of the present invention will be described withreference to the drawings.

FIG. 1 is a functional block diagram illustrating an example of anultrasonographic device according to the present invention. Firstly,description will be given of a flow of signal processing for imaging inthe ultrasonographic device. A transmission beam former 3 controlled bya controller 4 transmits transmission electrical pulses via atransmit/receive switch 2 to an ultrasonic probe 1 that is placed on thesurface of a subject to be examined. In this event, the transmissionbeam former is controlled such that delay times among channels of theprobe 1 can be adjusted to cause each ultrasonic beam to travel along adesired raster. Then, the ultrasonic probe 1 converts each electricalsignal transmitted from the transmission beam former 3 into anultrasonic signal, and transmits, as an ultrasonic pulse, the signalinto the subject to be examined. The ultrasonic pulse scattered in thesubject to be examined is partly transmitted back to and received by theultrasonic probe 1 as an echo signal. Then, the ultrasonic probe 1converts the echo signal into an electrical signal. The received signalsare transmitted to a receiving beam former 20 via the transmit/receiveswitch 2, and then stored in a memory 29 as data obtained by selectivelyamplifying the echo signals transmitted from a desired depth along thedesired rasters. An interpolation processor 30 interpolates data betweeneach adjacent two rasters from which actual data is obtained, andthereby increases the number of rasters. In this event, theinterpolation processor 30 selects the optimal interpolation method fromthe multiple methods as will be described later. The interpolated datais transmitted to the scan converter 23, which performs scan conversionon the data. The data after scan conversion is transmitted to a displayunit 24, which displays the data as an ultrasonogram.

Hereinbelow, interpolation and scan conversion according to the presentinvention will be described. Firstly, with reference to FIG. 2,description will be given of a scan conversion method from echo dataobtained by sector scanning or convex scanning into video image dataexpressed in an orthogonal coordinate system. In scan conversion, anintensity value at each data point (black circle in FIG. 2) after scanconversion is computed from intensity values of multiple data points(white circles in FIG. 2) before scan conversion surrounding the datapoint after scan conversion. In this event, no careful attention needsto be paid to what scan conversion method to employ if spatial samplingintervals in data after scan conversion are sufficiently wider thanthose in data before scan conversion. However, if spatial samplingintervals in data after scan conversion are not sufficiently wider thanthose in data before scan conversion, an output image is stronglyaffected by what scan conversion method to employ. Here, suppose thatscan conversion is performed in two stages: (1) sufficiently narrowingdown sampling intervals in data before scan conversion by usinginterpolation; and (2) performing scan conversion on the data. Then, theabove consideration on what scan conversion method to employ is boileddown to consideration on what interpolation method to employ.

To obtain an ultrasonic image, each of transmission and received beamsis focused. However, beam focusing in the horizontal direction islimited by the diffraction effect. In addition, rasters are typicallyarranged side by side in the horizontal direction at intervals eachhaving a length from a half to a quarter of a wavelength at the centerfrequency at the corresponding position. This is because too densearrangement of rasters in the horizontal direction will lead to decreasein frame rate. Consequently, each sampling interval in the horizontaldirection is around from 125 μm to 250 μm if, for example, the centerfrequency is 3 MHz. On the other hand, in the depth direction,sufficiently dense sampling with respect to the frequency of the carrieris performed. Accordingly, each sampling interval in the depth directionis computed to be around from 20 μm to 25 μm if it is assumed that anA/D converter normally performs sampling at 30 MHz to 40 MHz, and thatthe sound speed is 1500 m/s. As has been described, sampling density inthe azimuth direction is much lower than in the depth direction, andthus interpolation in a two-dimensional scan conversion can be dealtwith as a matter of one-dimensional interpolation in the azimuthdirection.

As one-dimensional interpolation methods, interpolation methods shown inFIGS. 3 and 4 are usually employed. FIG. 3 is a graph for illustratinglinear interpolation. In linear interpolation, an intensity value ofeach data point after interpolation is obtained through interpolationbetween intensity values of two closest points respectively positionedat both sides of the data point to be obtained after interpolation.Meanwhile, FIG. 4 is a graph for illustrating sinc functioninterpolation using four or more data points. In sinc functioninterpolation, each data point is supposed to be a representative pointin a space having a finite width, and an interpolation coefficient isdetermined using a sinc function obtained as a Fourier transform of arectangular function representing the finite width. As an intensityvalue of interpolated data at a position indicated by the dashed line inFIG. 4, an interpolated value is obtained by adding up the followingvalues: values obtained by multiplying intensity values of pixelsadjacent to the position by a coefficient of 2/π; and values obtained bymultiplying intensity values of pixels outwardly adjacent to theserespective pixels by a coefficient of −3/2π. Here, the coefficient of2/π is obtained from sinc functions (indicated by the solid line and thedotted line in FIG. 4) that reach their peaks at the aforementionedadjacent pixels, respectively. Meanwhile, the coefficient of −3/2π isobtained from sinc functions (indicated by the dashed line and thealternate long and short dash line in FIG. 4) that reach their peaks atthe aforementioned pixels outwardly adjacent to these adjacent pixels,respectively.

It is known that use of these interpolation methods deliversubstantially the same outcome, if interpolation-target data has asufficiently higher sampling frequency than its data frequency, but thatuse of interpolation using sinc functions delivers more accurateoutcome, if interpolation-target data does not have a sufficientlyhigher sampling frequency than its data frequency. If an interpolatedposition is fixed, interpolation coefficients (such as 2/π and −3/2πdescribed above) previously computed using sinc functions can beemployed. Accordingly, no computational load attributable to use of sincfunctions is generated. However, if the interpolated position isvariable, it is necessary to compute interpolation coefficients for eachinterpolated position by using sinc functions, which generates muchcomputational load. For practical purposes, such calculation does notnecessarily require use of sinc functions, though. Instead, each sincfunction may be approximated by Taylor expansion and then truncated tothe finite number of terms, and the above calculation may be performedusing the approximative functions thus obtained. Even this approach willpresent no practical problem. In particular, truncating each sincfunction to the finite number of terms is rather preferable since thisinterpolation processing needs to be performed at a speed higher than aframe rate of the ultrasonographic device on a DSP, in practical terms.In this case, when a sinc function is approximated by Taylor expansionabout a point a and truncated to terms up to the third-order, thefollowing is obtained:

Sin(a)/a+(x−a)(cos(a)/a−sin(a)/a²)+(x−a)²/2x(−sin(a)/a−2 cos(a)/a²+2cos(a)/a³)+(x−a)³/6x(−cos(a)/a+3 sin(a)/a²+4 cos(a)/a³−2 sin(a)/a³−6cos(a)/a⁴).

Accordingly, when expanded about a point of π/2 in the same manner, thesinc function can be approximated by the following at most third-orderpolynomial:(2/π²−8/3π³)x ³+(4/π²−4/π)x ²+(−4/π²−2/π+5/2)x+(4/π−π/2+1/3).The center value for expansion should preferably vary in accordance withan interpolated position, such as one set in interpolation from adjacentpoints, or in interpolation from points two points from the interpolatedposition. This can reduce necessary orders for the expansioncoefficient.

An ultrasonic image is a convolution of a point spread function specificto imaging conditions of a device, and a scatterer distribution in asubject. Here, the imaging conditions are determined by beam forming andpost processing. In the current ultrasonography, transmission beamforming and receiving beam forming are individually performed, and onlya single point is focused on and thus the other points are focused offin each raster in the transmission beam forming. This is because, in thetransmission beam forming, prevention of frame rate decrease isconsidered as a higher-priority issue than achievement of uniform focus.On the other hand, a configuration allowing a so-called dynamic focus inthe receiving beam forming has been implemented. In the dynamic focus,focus points are continuously transitioned in accordance with receivingtimings so that each raster image is uniformly focused on in the depthdirection.

A critical lateral blurring might occur in an echo from a deep part of aliving body, and a spread of a point spread function, that is, anazimuth resolution, in such a deep part depends on a receiving beamwidth. A beam width BW at a focus point depends on diffraction effect ofultrasonic waves rather than on the geometrical beam width, as shown inFIG. 5. A diffraction angle θ can be approximated by the followingequation. Here, λ is a wavelength, and D is an aperture diameter width.θ=sin⁻¹(λ/D)

A beam width will be computed by using a typical example below. Assumethat a center frequency in a deep part is 2 MHz, an aperture diameterwidth of 12.5 mm, and a distance from a probe surface to animaging-target site is Z. Here, the aperture diameter width is obtainedby approximating a transmission aperture diameter weighting value at ahalf. Then, the diffraction angle is 0.06 rad, and thus the beam widthis 0.06×Z mm. Meanwhile, a typical raster width is on the order of0.01×Z mm. This means that a sampling interval is approximately sixtimes narrower than the beam width, and thus linear interpolation willbe good enough in typical cases.

FIG. 6 shows experimental results. In each graph, the vertical axisindicates intensity while the horizontal axis indicates a horizontalposition. In FIG. 6( a), which shows data on echo signals from pointreflectors, the black circles connected by the solid line correspond todata points sampled by double density scanning while the white circlesconnected by the dotted line correspond to data points obtained byfirstly sampling data by normal scanning and then doubling the densityof the sampled data by linear interpolation. There is little differencein these two experimental results, and thus these lines overlap eachother in most parts. This indicates that the linear interpolation causessubstantially no image degradation in this case. The result matches whatis estimated to occur in the case where a beam width is sufficientlygreater than a raster width as described above.

Meanwhile, in FIG. 6( b), which shows data on speckle signals, the solidline corresponds to data points sampled by double density scanning whilethe dotted line corresponds to data points obtained by firstly samplingdata by normal scanning and then doubling the density of the sampleddata by linear interpolation. As is clear from FIG. 6( b), signals withhigh spatial frequency components are lost in the experimental resultobtained by linear interpolation. As a result, a cross-sectional imageobtained by linear interpolation will give an impression of sufferingfrom a lateral blurring.

Firstly, review will be made on what causes the result different fromthe initial estimation, that is, the result that linear interpolation onspeckle signals caused some lateral blurring while sinc functioninterpolation on speckle signals caused no lateral blurring. This resultis explainable by the fact that a signal dynamic range is limited.Specifically, the existing ultrasonographic device has a limited dynamicrange, such as 150 dB of an A/D converter. Accordingly, if a scattererhas high reflection intensity, a beam width is equal to the spatialwidth of the reflected signal as shown in FIG. 7. However, suppose thecase where a reflected signal has relatively low reflection intensitysuch as a speckle signal, and is reduced in signal intensity such as anecho signal from a deep part since an ultrasonic beam is attenuatedwhile propagating in a living body. In this case, the signal has anarrow width in the effective dynamic range as shown in FIG. 8. Asdescribed above, in such a case as shown in FIG. 8, it is necessary toemploy, as an interpolation method, sinc function interpolation ratherthan linear interpolation.

FIG. 9 shows principle experiment results of evaluating an effect ofnoise on data after interpolation. Here, evaluation is made byapproximating an ideal beam before noise addition by a sinc function,and using virtual data with noise obtained by adding noise like a deltafunction to the approximated function. In FIG. 9( a), which shows datawithout noise, the solid line, the dotted line and the dashed lineindicate ideal beam data, linear-interpolated data andsinc-function-interpolated data, respectively. If original data containsno noise, the sinc-function-interpolated data comes closer to theoriginal data than the linear-interpolated data. FIG. 9( b) shows aresult obtained by adding noise at the position indicated by the arrowin the FIG. 9( b). As is clear from FIG. 9( b), a range where an effectof noise is present is different between the sinc-function-interpolateddata and the linear-interpolated data. Specifically, if linearinterpolation is used for data including some noise, the range where theeffect of the noise is present in the interpolated data is limited tointerpolated points adjacent to the position containing the noise. Bycontrast, if sinc function interpolation is used for data containingsome noise, the effect of the noise reaches not only interpolated pointsadjacent to the position containing the noise but also interpolatedpoints adjacent to these adjacent points.

In Table 1, results of simulation evaluation on effect of theinterpolation method on noise are summarized. In the evaluation, 20 log(error in sinc function interpolation/error in linear interpolation) wascomputed. In Table 1, each positive dB value indicates that linearinterpolation is more preferable while each negative dB value indicatesthat sinc function interpolation is more preferable.

TABLE 1 sampling S/N λ/2.5 λ/10 λ/40 1     0 dB +2.5 dB +2.8 dB 2 −1.4dB +2.5 dB +3.2 dB 10   −2 dB +2.5 dB +6.8 dB

The results show that sinc function interpolation will expand an effectof noise to more points than linear interpolation since sinc functioninterpolation uses adjacent four data points while linear interpolationuses only adjacent two data points. Thus, the results show that linearinterpolation is superior to sinc function interpolation as aninterpolation method for data obtained by sufficiently dense samplingwith respect to the frequency component of the signal.

The conclusion drawn from these results is that an adaptive transitionof interpolation method for each signal as shown in FIG. 10 will beeffective. Specifically, in this adaptive method, linear interpolationis applied to a signal having a wide effective beam width in a dynamicrange while sinc function interpolation is applied to a signal having anarrow effective beam width in the dynamic range. Signal intensity andan electrical noise level specific to each device will not change muchonce a subject, and its site to be examined and a transmission focuspoint are determined. Thus, a transition depth can be preset for eachmode.

Typically, an imaging-target site is selected upon selection ofultrasonic probe connected to an ultrasonographic device. For example,in the case of a low-frequency probe for convex scanning, optimalimaging parameters are selected for each of target sites such as aliver, a kidney, an uterus, an embryo and an aorta. The selection andswitching of a parameter set will be hereinbelow referred to as modeswitching. The attenuation rate of an ultrasonic wave during propagationvaries greatly depending on target sites described above. For example,if the target site is an embryo, most of the propagation medium is madeof an amniotic fluid, and thus an ultrasonic wave from the site islittle attenuated. On the other hand, if, for example, the target siteis a liver, most of the propagation passage is substantially occupied bysubcutaneous fat and the liver, which is an organ, so that theattenuation rate of an ultrasonic wave propagating through the passageis far higher than through an amniotic fluid. Hence, the slope of anecho signal relative to depth varies depending on modes as shown in FIG.11. Thus, the depth where a beam width is greater than a raster width inthe dynamic range also varies depending on modes. Note that theattenuation rate of an ultrasonic wave from an imaging-target site alsovaries depending on the type and extent of disease (if the target siteis a cirrhotic liver, for example). Thus, the ultrasonographic deviceshould preferably allow fine mode adjustment applicable to variousconditions including lesions and extents of disease.

In the light of these circumstances, an ultrasonographic deviceaccording to one of the embodiments includes a depth storage unit 21,and previously stores, in the depth storage unit 21, data oninterpolation method transition depths, that is, data on depths forswitching between linear interpolation and sinc function interpolation.The interpolation processor 30 refers to the interpolation methodtransition depth data stored in the depth storage unit 21. Then, theinterpolation processor 30 interpolates a data point by the linearinterpolation method when the data point has a depth less than thestored interpolation method transition depth for the employed mode, butinterpolates the data point by the sinc function interpolation methodwhen the data point has a depth more than this stored interpolationmethod transition depth. Here, if the interpolation method is switchedat a switching depth in a manner of steeply transitioning from linearinterpolation to sinc function interpolation, the transition point mightappear as an artifact This is prevented by a method using continuouslyvarying weighting values to be described below. Specifically, assumethat a linear-interpolated value is I1, a weighting value for I1 is w1,a sinc-function-interpolated value is Is, and a weighting value for Isis ws. Then, an output I of an interpolation result using theseweighting values w1 and ws is expressed by the following expression. Theultrasonographic device may be configured to allow the weighting valuefor use to continuously transition between w1 and ws about a transitionpoint as show in FIG. 12.I=w1×I1+ws×Is

Note that, an interface of transition point control 33 may be providedin a control panel of a diagnostic system 32 so as to allow an operatorto control the position of this transition point as he/she likes. Inthis case, the ultrasonographic device may allow the operator either tomove the transition point in the depth direction or to change theaforementioned ratio of w1 to ws. FIG. 13 shows an ultrasonographicdevice allowing an operator to set a transition point by inputting asingle parameter indicating a position in the depth direction, whileFIG. 14 shows an ultrasonographic device allowing an operator to setconditions for changing the w1-to-ws ratio by inputting two parametersof a transition point (indicated by “a” in FIG. 14) and a slope ofchanging the w1-to-ws ratio during the transition (indicated by “b” inFIG. 14).

In the device shown in FIG. 1, the position of the interpolationtransition point is fixed once an imaging mode is determined. However,actually, the optimal position of the transition point for a certainimaging-target site varies greatly depending on variation among subjectsto be examined. For example, if the imaging-target site is a lever, theoptimal transition position greatly depends on conditions including anextent of disease progress such as cirrhosis, and a subcutaneous fatthickness. Thus, the optimal position of the transition point shouldpreferably be set for each obtained signal. In particular, anultrasonographic device has a function called a time gain control, andthus is capable of adjusting a gain on the signal depth basis. Hence, asignal width in the dynamic range is not always completely determined byparameters (such as a target site, a focus point of transmission beamand a transmit/receive frequency) for determining an imaging mode of thedevice.

FIG. 15 shows a configuration example of the device applicable to thiscase. The device of this embodiment is the same as the device shown inFIG. 1 except that this device including an interpolation method settingunit 22, and thus description for the same parts as the foregoing devicewill be omitted. Upon receipt of an input of interpolation-targetone-dimensional data obtained along a raster, the interpolation methodsetting unit 22 computes a signal intensity change profile in the depthdirection as shown in FIG. 10. Then, based on the change profile, theinterpolation method setting unit 22 obtains a point at which the signalintensity goes below the preset dynamic range. This point will behereinbelow referred to as interpolation transition point. In FIG. 10,the solid line indicates an actual data while the dotted line indicatesdata on a computed trend of the actual data in the depth direction. Thistrend may be computed by applying a filter such as a low-pass filter ora median filter on data indicated by the solid line. Alternatively, thetrend may be computed by assuming that an echo intensity I(x) at a depthx is expressed by I(x)=ax+b, and obtaining the coefficients a and b byleast-square fitting.

If the interpolation method transition point varies from one raster toanother, artifacts appear in a stripe pattern in a resultant image. Thismay be prevented by such a method as obtaining a signal intensity changeprofile of an average data among multiple rasters or using an averagevalue obtained by averaging the interpolation transition points computedfor the respective rasters.

As another embodiment, FIG. 16 shows a configuration example of thedevice applicable to the case where data is interpolated after improvingthe S/N ratio thereof by using a signal processing technique. Thespecific processing performed by a noise reduction filter 31 shown inFIG. 16 will be described in detail with reference to FIGS. 17 to 19.

FIG. 17 illustrates a target pixel for noise reduction processing and anarea used for computing weighting values for the pixel. FIG. 18 is aflowchart of the noise reduction processing.

A memory in the noise reduction filter 31 stores therein two-dimensionaldata formed of N one-dimensional image data sets each changing in thedirection of the time axis t as shown in FIG. 17. Specifically, theseone-dimensional image data sets respectively correspond to 1st, 2nd, . .. , N-th rasters and are arranged side by side in the direction wherethe rasters are arranged.

Firstly, an area (weighting value computing area) surrounding the targetpixel to be used for computing weighting values for a noise-reductionprocessing target pixel (intensity I₀) are set. The weighting valuecomputing area includes pixels (i_(max)×j_(max) pixels each having anintensity I_(ij)) surrounding the target pixel, where i=1, 2, . . . ,i_(max) and j=1, 2, . . . , j_(max). The larger the weighting valuecomputing area, the more effective the obtained noise reduction filteris but the lower the required computation speed is. The values ofi_(max) and j_(max) and a shape of a weighting function are set througha parameters set process 101 shown in FIG. 18. In the parameters setprocess 101, a difference between the intensity I₀ of the target pixeland the intensity I_(ij) of each pixel in the weighting value computingarea surrounding the target pixel is computed. This computation isperformed on each target pixel of this processing. Then, a histogram ofthe computed differences of intensity is created, and a width of thehistogram is computed. This width of the histogram of the differences ofintensity is used for setting a weighting function to be describedlater.

Then, in a weighting value computing area setting process 102, set arepixels in the weighting value computing area, which is defined by theposition of the target pixel and the values of i_(max) and j_(max). In aweighting value computing process 103, weighting values are computed byusing weighting functions to be described later. If it is decided thatthis process has been performed on all the pixels in the weighting valuecomputing area in a weighting value computation completion decisionprocess 104, an intensity value to be assigned to the target pixel inthe foregoing process of setting computing area is computed in anintensity adjustment process 105. If, in a target pixel completiondecision process 106, it is decided that the target pixel position hasbeen shifted in the foregoing process of setting computing area till theintensity values are computed for all the pixels in a two-dimensionaldata for extracting a structure of a body tissue, the noise reductionprocessing is completed.

FIG. 19 illustrates the aforementioned weighting function. FIG. 19( a)shows the histogram of the aforementioned differences of intensityobtained from a typical ultrasonic image. In FIG. 19( a), the horizontalaxis indicates aforementioned difference of intensity (I₀−I_(ij)) whilethe vertical axis indicates appearance count of difference of intensity(I₀−I_(ij)). FIG. 19( b) shows an example of a weighting function W inwhich a weighting value decreases monotonically with the increase of theabsolute value of difference of intensity (I₀−I_(ij)). A weighting valueW_(ij) is computed for each value on the horizontal axis (I₀−I_(ij)).Examples of the weighting function W include not only an even-orderedpolynomial but also various even functions such as Gaussian function andthe function of 1/(x²+a²). The function W takes a local maximum pointwith (I₀−I_(ij))=0, and the integration value of the absolute value W ofthe function from the negative infinity to the positive infinity isfinite. Based on the weighting function, the intensity value of thetarget pixel is computed by the following expression:I₀+Σ{(I_(ij)−I₀)W_(ij)}/ΣW_(ij).

This noise reduction filter functions differently depending on theintensity continuity from the target pixel to the surrounding pixels.The noise reduction filter functions as a two-dimensional low-passfilter, when the difference between the intensity I₀ and the intensityI_(ij) of each surrounding pixel is small. This is because asubstantially constant weighting value is assigned to the target pixelin this case. On the other hand, the noise reduction filter functions asa one-dimensional low-pass filter and an all-pass filter, when the pixelhaving the intensity I₀ is positioned at an interface between twostructures (tissues). Specifically, the noise reduction filter functionsas a one-dimensional low-pass filter in the direction parallel to theinterface between the two structures, since a high weighting value isassigned to each pixel positioned on the interface. Meanwhile, the noisereduction filter functions as an all-pass filter in the directionperpendicular to the interface between the two structures. Accordingly,the noise reduction filter will never dampen sharpness at an interface.As described above, the ultrasonographic device according to thisembodiment employs a nonlinear filter that functions differentlydepending on the intensity distribution profile of the pixels. Thisallows the ultrasonographic device to perform noise reduction processingwhile minimizing an effect of dampening edges in an image, and thusenlarges an effective dynamic range for each signal. Thereby, theultrasonographic device can shift the transition point from linearinterpolation to sinc function interpolation to a deeper part.

INDUSTRIAL APPLICABILITY

The present invention makes it possible to provide an ultrasonogramhaving a preferable spatial resolution and signal/noise ratio.

The invention claimed is:
 1. An ultrasonographic device comprising: anultrasonic probe configured to transmit an ultrasonic beam to an objectand receive an ultrasonic echo signal; a transmission beam formerconfigured to transmit a signal to the ultrasonic probe so as to causethe ultrasonic probe to emit the ultrasonic beam for scanning a sectoralregion; a receiving beam former configured to receive echo signals froma plurality of data points on rasters in the sectoral region scanned bythe ultrasonic probe; a memory configured to store therein, the echosignals from the plurality of data points as data sets for displaying anultrasonogram; an interpolation processor configured to interpolate adata set between each adjacent two rasters in the data sets stored inthe memory, where the interpolation processor interpolates the data setby using a plurality of interpolation methods used for differing depthsmeasured with respect to a surface of the object, respectively, whereinthe plurality of interpolation methods include linear interpolationapplied in interpolating data sets positioned closer to the surface ofthe object than a switching depth, and sinc function interpolationapplied in interpolating data sets positioned farther away from thesurface of the object than the switching depth; and an inputterconfigured to receive an input of the switching depth representing apreset interpolation method transition depth set with respect to thesurface of the object, where switching transition between the pluralityof interpolation methods is performed.
 2. The ultrasonographic deviceaccording to claim 1, wherein the plurality of interpolation methodsinclude two-or-more dimensional function interpolation.
 3. Theultrasonographic device according to claim 2, wherein an interpolateddata set is computed as a weighted sum of a first data set interpolatedby the linear interpolation and a second data set interpolated by thetwo-or-more dimensional function interpolation, and in this computation,when a data point depth is closer to the object surface than the presetinterpolation method transition depth, an increased weighting value isassigned to the first data set interpolated by the linear interpolation,and when the data point depth is farther from the object surface thanthe preset interpolation method transition depth, an increased weightingvalue is assigned to the second data set interpolated by the two-or-moredimensional function interpolation.
 4. The ultrasonographic deviceaccording to claim 1, wherein the ultrasonographic device performs noisereduction before interpolating the data set.
 5. The ultrasonographicdevice according to claim 1, wherein the ultrasonographic device storesa plurality of switching depths representing a plurality of presetinterpolation transition depths, where switching transition between theplurality of interpolation methods is performed.
 6. An ultrasonographicdevice comprising: an ultrasonic probe that transmits an ultrasonic beamto an object and receives an ultrasonic echo signal; a transmission beamformer which transmits a signal to the ultrasonic probe so as to causethe ultrasonic probe to emit the ultrasonic beam for scanning a sectoralregion; a receiving beam former which receives echo signals from aplurality of data points on rasters in the sectoral region scanned bythe ultrasonic probe; a memory which stores therein the echo signalsfrom the plurality of data points as data sets for displaying anultrasonogram; an interpolation method setter configured to compute asignal intensity change profile of each echo signal in the depthdirection of the object along the corresponding raster, and which sets,as a preset interpolation method transition depth, a depth pointmeasured from a surface of the object, at which the signal intensitygoes below a preset signal intensity level; and an interpolationprocessor which interpolates a data set between each adjacent tworasters in the data sets stored in the memory, by interpolating datapoints positioned closer to the surface of the object than the presetinterpolation method transition depth by linear interpolation, and byinterpolating data points positioned farther away from the surface ofthe object than the preset interpolation method transition depth by asinc function interpolation.
 7. The ultrasonographic device according toclaim 6, wherein the interpolated data set is computed as a weighted sumof a first data set interpolated by the linear interpolation and asecond data set interpolated by the sinc function interpolation, and inthis computation, when a data point depth is closer to the surface ofthe object than the preset interpolation method transition depth, anincreased weighting value is assigned to the first data set interpolatedby the linear interpolation, and when the data point depth is fartherfrom the surface of the object than the preset interpolation methodtransition depth, an increased weighting value is assigned to the seconddata set interpolated by the sinc function interpolation.
 8. Theultrasonographic device according to claim 6, wherein furthercomprising: a weighter unit configured to assign weighting valuesrespectively to the first data set interpolated by the linearinterpolation and the second data set interpolated by the sinc functioninterpolation, and combining the weighted data sets; and an inputterconfigured to receive a control of the weighting values.
 9. Theultrasonographic device according to claim 6, further comprising: adepth storage configured to store transition depth data, and wherein theinterpolation processor references the transition depth data todetermine which interpolation method to use for a given depth asmeasured from the surface of the object.
 10. The ultrasonographic deviceaccording to claim 1, further comprising: a depth storage configured tostore transition depth data, and wherein the interpolation processorreferences the transition depth data to determine which interpolationmethod to use for a given depth, to interpolate the data set.
 11. Theultrasonographic device according to claim 1, wherein an interpolateddata set is computed as a weighted sum of a first data set interpolatedby the linear interpolation and a second data set interpolated by thesinc function interpolation, and in this computation, when a data pointdepth closer to the surface of the object than the preset interpolationmethod transition depth, an increased weighting value is assigned to thefirst data set interpolated by the linear interpolation, and when thedata point depth farther from the surface of the object than the presetinterpolation method transition depth, an increased weighting value isassigned to the second data set interpolated by the sinc functioninterpolation.